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Real-time multiple object instances detection
Proceedings of the 20th ACM international conference on Multimedia, 2012In this paper, we present a novel, real-time multiple object instance detection system via template matching and pairwise classification. Instance detection aims to find and locate exactly the same object instances as specified. Our system is composed of two heterogeneous stages. The first stage adopts instance-specific detection to generate candidates.
Chengli Xie +3 more
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Multiple zone object detection system
The Journal of the Acoustical Society of America, 1985Apparatus utilizing a combination energy transmitting and energy receiving transducer or a transducer set consisting of separate energy transmitting and energy receiving transducers, are employed to detect and subsequently indicate the presence of an object(s) within one or more of a plurality of spaced-apart spacial zones.
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Multiple Object Detection Mechanism Using YOLO
2020Object detection is a computer technology which relates with image processing and computer technology. There are many cases where in a given situation there is a need for faster object detection. For example, consider a traffic scenario or a case of natural disaster. In such areas, the detection of humans or specified objects becomes difficult. In such
G. A. Vishnu Lohit, Nalini Sampath
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Object detection by multiple textural analyzers
Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 2003A Genetic Programming algorithm using discrete Fourier transforms is used to evolve an automatic object detector of vehicles for infrared images. Results show promise for the solution of a real world problem.
D. Howard, S.C. Roberts
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Layered detection for multiple overlapping objects
Object recognition supported by user interaction for service robots, 2003This paper describes a method for detecting multiple overlapping objects from a real-time video stream. Layered detection is based on two processes: pixel analysis and region analysis. Pixel analysis determines whether a pixel is stationary or transient by observing its intensity over time.
H. Fujiyoshi, T. Kanade
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Detection, Tracking and Avoidance of Multiple Dynamic Objects
Journal of Intelligent and Robotic Systems, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Madhava Krishna, K., Kalra, Prem K.
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Automating Snakes for Multiple Objects Detection
2011Active contour or snake has emerged as an indispensable interactive image segmentation tool in many applications. However, snake fails to serve many significant image segmentation applications that require complete automation. Here, we present a novel technique to automate snake/active contour for multiple object detection.
Baidya Nath Saha +2 more
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Detection of occluded multiple objects using occlusion activity detection and object association
Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004., 2005This paper proposes the detection of occluded moving objects using occlusion activity detection and an object association algorithm. When multiple objects are occluded between them, a simultaneous feature based tracking of multiple objects using tracking filters fails. To estimate feature vectors such as location, color, velocity, and acceleration of a
null Heungkyu Lee, null Hanseok Ko
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3D Object Detection with Multiple Kinects
2012Categorizing and localizing multiple objects in 3D space is a challenging but essential task for many robotics and assisted living applications. While RGB cameras as well as depth information have been widely explored in computer vision there is surprisingly little recent work combining multiple cameras and depth information. Given the recent emergence
Susanto, W. +2 more
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Multiple-Kernel, Multiple-Instance Similarity Features for Efficient Visual Object Detection
IEEE Transactions on Image Processing, 2013We propose to use the similarity between the sample instance and a number of exemplars as features in visual object detection. Concepts from multiple-kernel learning and multiple-instance learning are incorporated into our scheme at the feature level by properly calculating the similarity. The similarity between two instances can be measured by various
Chensheng, Sun, Kin-Man, Lam
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